{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-08 14:23:34.950527: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-12-08 14:23:34.950577: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "\n",
    "from keras.datasets import mnist\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "fig, axes = plt.subplots(2,5,figsize=(10,4)) #新建一个有10张子图的， 2行5列的画布\n",
    "axes = axes.flatten() #axes中存储了每一个子图\n",
    "for i in range(10):\n",
    "    axes[i].imshow(x_train[i], cmap=\"gray_r\") #将x_train的第i张图， 画在第1个子图上，反灰度图\n",
    "    axes[i].set_xticks([])  #移除图像的x， y轴刻度\n",
    "    axes[i].set_yticks([])\n",
    "plt.tight_layout() #采用更紧凑美观的布局方式\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10,10,figsize=(20,20)) #新建一个有100张子图的， 10行10列的画布\n",
    "\n",
    "for i in range(10):\n",
    "    indice = np.where(y_train==i)[0]\n",
    "    for j in range(10):\n",
    "        axes[i][j].imshow(x_train[indice[j]],cmap=\"gray_r\")\n",
    "        axes[i][j].set_xticks([])  #移除图像的x， y轴刻度\n",
    "        axes[i][j].set_yticks([])\n",
    "plt.tight_layout() #采用更紧凑美观的布局方式\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "dlopen: cannot load any more object with static TLS\n___________________________________________________________________________\nContents of /home/wenbin/.pyenv/versions/3.9.7/lib/python3.9/site-packages/sklearn/__check_build:\nsetup.py                  __init__.py               _check_build.cpython-39-x86_64-linux-gnu.so\n__pycache__\n___________________________________________________________________________\nIt seems that scikit-learn has not been built correctly.\n\nIf you have installed scikit-learn from source, please do not forget\nto build the package before using it: run `python setup.py install` or\n`make` in the source directory.\n\nIf you have used an installer, please check that it is suited for your\nPython version, your operating system and your platform.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m~/.pyenv/versions/3.9.7/lib/python3.9/site-packages/sklearn/__check_build/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_check_build\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcheck_build\u001b[0m  \u001b[0;31m# noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: dlopen: cannot load any more object with static TLS",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_19425/3731201897.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneighbors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mknc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.7/lib/python3.9/site-packages/sklearn/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;31m# it and importing it first would fail if the OpenMP dll cannot be found.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_distributor_init\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__check_build\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_show_versions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshow_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.9.7/lib/python3.9/site-packages/sklearn/__check_build/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_check_build\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcheck_build\u001b[0m  \u001b[0;31m# noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m     \u001b[0mraise_build_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.pyenv/versions/3.9.7/lib/python3.9/site-packages/sklearn/__check_build/__init__.py\u001b[0m in \u001b[0;36mraise_build_error\u001b[0;34m(e)\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m             \u001b[0mdir_content\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m     raise ImportError(\n\u001b[0m\u001b[1;32m     32\u001b[0m         \"\"\"%s\n\u001b[1;32m     33\u001b[0m \u001b[0m___________________________________________________________________________\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: dlopen: cannot load any more object with static TLS\n___________________________________________________________________________\nContents of /home/wenbin/.pyenv/versions/3.9.7/lib/python3.9/site-packages/sklearn/__check_build:\nsetup.py                  __init__.py               _check_build.cpython-39-x86_64-linux-gnu.so\n__pycache__\n___________________________________________________________________________\nIt seems that scikit-learn has not been built correctly.\n\nIf you have installed scikit-learn from source, please do not forget\nto build the package before using it: run `python setup.py install` or\n`make` in the source directory.\n\nIf you have used an installer, please check that it is suited for your\nPython version, your operating system and your platform."
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "k=5\n",
    "knc=KNeighborsClassifier(n_neighbors=k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_train=x_train.shape[0]\n",
    "n_test=x_test.shape[0]\n",
    "print(x_train.shape)\n",
    "print(type(x_train))\n",
    "x_train_1=x_train.reshape(n_train,-1)  #-1表示最后一个维度让电脑自己算\n",
    "x_test_1=x_test.reshape(n_test, -1)\n",
    "print(x_train_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#开始训练\n",
    "import time\n",
    "\n",
    "start_time=time.time()\n",
    "print(\"pre fit:\" + str(start_time))\n",
    "\n",
    "knc.fit(x_train_1, y_train)\n",
    "\n",
    "end_time=time.time()\n",
    "print(\"fit done:\"+str(end_time))\n",
    "print(\"duration:\" + str(end_time-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_to_predict=1000\n",
    "y_predict=knc.predict(x_test_1[0:n_to_predict])\n",
    "print(y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y_predict[0:n_to_predict].shape)\n",
    "print(y_test[0:n_to_predict].shape)\n",
    "accuracy= np.sum(y_predict[0:n_to_predict]==y_test[0:n_to_predict])/ n_to_predict\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_id=np.random.choice(np.where(y_predict[0:n_to_predict] !=y_test[0:n_to_predict])[0], size=10)\n",
    "print(error_id)\n",
    "fig, axes=plt.subplots(2,5, figsize=(10,4))\n",
    "# print(axes.shape)\n",
    "axes=axes.flatten()\n",
    "# print(axes.shape)\n",
    "for i, idx in enumerate(error_id):\n",
    "    axes[i].imshow(x_test[idx].reshape(28,28), cmap=\"gray_r\")\n",
    "    axes[i].set_xticks([])\n",
    "    axes[i].set_yticks([])\n",
    "    axes[i].set_title(\"y_predict: %d\\ny_test:%d\" % (y_predict[idx], y_test[idx]))\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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